Automatic image enhancement using computed predictors

ABSTRACT

A method and apparatus for enhancing electronic images allows for improved characteristics between light areas and dark areas, and is particularly effective for backlit images. A transition between light and dark image portions is detected. A determination is made from an analysis of spectral distributions as to whether an image portion is backlit. Upon detection, image data is adjusted to lighten or darken image portions to allow for improved image viewing. Use of cumulative probability distribution data associated with an electronic image facilitates isolation of backlit image portions and object image portions.

This application is a Continuation-In-Part of U.S. patent applicationSer. No. 11/453,182, filed Jun. 14, 2006, under Express Mail Label No.EV 515354723 US.

BACKGROUND OF THE INVENTION

The subject application is directly broadly to image enhancement, and isparticularly applicable to captured images of backlit specimens.However, it will be appreciated that the concepts disclosed herein areparticularly applicable to any image enhancement wherein two or moreportions of captured image have distinct lighting, brightness, orcontrast characteristics.

Electronically encoded images are ubiquitous. Today, such images may becaptured directly from a device, such as a digital still camera ordigital video recorder, scanned in from other media, such asphotographs, captured from streaming media, such as a live televisionfeed, or consist of one or more previously obtained images retrievedfrom storage, such as from numerically encoded image archives. Many suchimages were either captured under less-than-ideal conditions, or withequipment that rendered a resulting image less than optimal due tovariations in lighting or other properties on various aspects of acaptured image. One example is images that are taken in a backlitsetting. Such a situation may result when a bright sky, direct sunlight,or any other relatively intense background illumination source issituated behind an object of interest, such as a building, person orlandscape feature. The background illumination in such a situation issufficiently intense that detail or resolution of the foreground imageor object, the backlit image portion, or both is compromised. Earlierapproaches to address such concerns have been made algorithmically,electrically, via signal processing or mechanically (such as throughfiltration, f-stop, aperture size, and the like during image capture).However, earlier systems focused on capture or processing of an image asa whole, such that attempts to address concerns for one portion of animage would adversely impact other aspects of the image.

Captured or stored images are typically stored in an encoded format,such as digitally, which encoding is often done in connection withcomponent values of a primary color space. Such color components aresuitably additive in nature, such as red-green-blue (RGB), orsubtractive, such as cyan, yellow, magenta (CYM), the latter of which isfrequently coupled with a black color (K), referred to as CYMK orCYM(K). Additive primary color space descriptions are generallyassociated with images displayed on light generating devices, such asmonitors or projectors. Subtractive primary color space descriptions aregenerally associated with images generated on non-light generatingdevices, such as paper printouts. In order to move an image from adisplay to a fixed medium, such as paper, a conversion must be madebetween color spaces associated with electronic encoding of documents.

The concepts disclosed herein are better appreciated with anunderstanding of various numeric models used to represent images, andimage colorization, in image processing or rendering applications. Oneof the first mathematically defined color spaces was the CIE XYZ colorspace (also known as CIE 1931 color space), created by CIE in 1931. Ahuman eye has receptors for short (S), middle (M), and long (L)wavelengths, also known as blue, green, and red receptors. One need onlygenerate three parameters to describe a color sensation. A specificmethod for associating three numbers (or tristimulus values) with eachcolor is called a color space, of which the CIE XYZ color space is oneof many such spaces. The CIE XYZ color space is based on directmeasurements of the human eye, and serves as the basis from which manyother color spaces are defined.

In the CIE XYZ color space, tristimulus values are not the S, M and Lstimuli of the human eye, but rather a set of tristimulus values calledX, Y, and Z, which are also roughly red, green and blue, respectively.Two light sources may be made up of different mixtures of variouscolors, and yet have the same color (metamerism). If two light sourceshave the same apparent color, then they will have the same tristimulusvalues irrespective of what mixture of light was used to produce them.

CIE L*a*b* (CIELAB or Lab) is frequently thought of as one of the mostcomplete color models. It is used conventionally to describe all thecolors visible to the human eye. It was developed for this specificpurpose by the International Commission on Illumination (CommissionInternationale d'Eclairage, resulting in the acronym CIE). The threeparameters (L, a, b) in the model represent the luminance of the color(L: L=0 yields black and L=100 indicates white), its position betweenred and green (a: negative values indicate green, while positive valuesindicate red), and its position between yellow and blue (b: negativevalues indicate blue and positive values indicate yellow).

The Lab color model has been created to serve as a device independentreference model. It is therefore important to realize that visualrepresentations of the full gamut (available range) of colors in thismodel are not perfectly accurate, but are used to conceptualize a colorspace. Since the Lab model is three dimensional, it is representedproperly in a three dimensional space. A useful feature of the model isthat the first parameter is extremely intuitive: changing its value islike changing the brightness setting in a TV set. Therefore only a fewrepresentations of some horizontal “slices” in the model are enough toconceptually visualize the whole gamut, wherein the luminance issuitably represented on a vertical axis.

The Lab model is inherently parameterized correctly. Accordingly, nospecific color spaces based on this model are required. CIE 1976 L*a*b*or Lab mode is based directly on the CIE 1931 XYZ color space, whichsought to define perceptibility of color differences. Circularrepresentations in Lab space correspond to ellipses in XYZ space.Non-linear relations for L*, a*, and b* are related to a cube root, andare intended to mimic the logarithmic response of the eye. Coloringinformation is referred to the color of the white point of the system.

Electronic documents, such as documents that describe color images, aretypically encoded in one or more standard formats. While there are manysuch formats, representative descriptions currently include MicrosoftWord file (*.doc), tagged information file format (“TIFF”), graphicimage format (“GIF”), portable document format (“PDF”), Adobe Systems'PostScript, hypertext markup language (“HTML”), extensible markuplanguage (“XML”), drawing exchange files (*.dxf), drawing files (*.dwg),Paintbrush files (*.pcx), Joint Photographic Expert Group (“JPEG”), aswell as a myriad of other bitmapped, encoded, compressed or vector fileformats.

It would be advantageous to have a system and method that allowed forready conversion of any such encoded images to address loss of imagequality associated with portions of an image being subject to differentillumination or lighting characteristics.

SUMMARY OF THE INVENTION

In accordance with the subject application, there is provided a systemand method for image enhancement.

Further, in accordance with the subject application, there is provided asystem and method for image enhancement wherein two or more portions ofcaptured image have distinct lighting, brightness, or contrastcharacteristics.

Still further, in accordance with the subject application, there isprovided a system and method that allows for ready conversion of anysuch encoded images to address loss of image quality associated withportions of an image being subject to different illumination or lightingcharacteristics.

Still further, in accordance with the subject application, there isprovided a system for predictor-based image enhancement. The systemcomprises means adapted for receiving image data, the image dataincluding data representative of a backlit image inclusive of at leastone specimen area and at least one background area. The system furthercomprises transition detection means adapted for determining, fromreceived image data, a transition between the at least one specimen areaand the at least one background area. The system also comprisesadjustment means adapted for adjusting a parameter of image dataassociated with at least one of the specimen area and the backgroundarea in accordance with a determined transition.

In one embodiment, the adjustment means includes means adapted foradjusting a lighting level associated with at least one of image data ofthe specimen area and image data of the background area. In anotherembodiment, the adjustment means includes means adapted for increasing alighting level associated with image date of the specimen area anddecreasing a lighting level associated with image data of the backgroundarea.

In a further embodiment, the system further comprises determining meansadapted for determining spectral frequency data representative of aspectral frequency distribution of color data included in the imagedata. In addition, the adjustment means includes means adapted foradjusting the lighting level associated with at least one of image dataof the specimen area and image data of the background area in accordancewith the spectral frequency data. Preferably, the spectral frequencydata includes distribution data representative of a cumulativeprobability distribution of intensity values encoded in the image data.

In yet another embodiment, the system also comprises mask generatormeans adapted for generating mask data corresponding to a determinedtransition. In this embodiment, the adjustment means includes meansadapted for selectively adjusting a parameter of image data associatedwith at least one of the specimen area and the background area inaccordance with a determined transition in accordance with the maskdata. Preferably, the mask data corresponds to at least one portion ofan image represented by the image data, which at least one portiondefines a shape having no significant holes or discontinuities.

Still further, in accordance with the subject application, there isprovided a method for predictor-based image enhancement in accordancewith the system described above.

Still other advantages, aspects and features of the subject applicationwill become readily apparent to those skilled in the art from thefollowing description wherein there is shown and described a preferredembodiment of this subject application, simply by way of illustration ofone of the best modes best suited to carry out the subject application.As it will be realized, the subject application is capable of otherdifferent embodiments and its several details are capable ofmodifications in various obvious aspects all without departing from thescope of the subject application. Accordingly, the drawings anddescriptions will be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates representative platforms for performing imageenhancement in connection with the subject application;

FIG. 2 is a flow chart for performing the image enhancement of thesubject application;

FIGS. 3A and 3B illustrate graphically spectral frequency dataassociated with an input for backlit images;

FIGS. 4A and 4B illustrate graphically spectral frequency dataassociated with an input for frontlit images;

FIG. 5 illustrates an output associated with a normal backlit image;

FIG. 6 illustrates a mask isolating portions of the image of FIG. 5;

FIG. 7 illustrates an enhancement to the image of FIG. 5 afterapplication of teachings of the subject application; and

FIG. 8 illustrates graphically spectral frequency data associated with acorrected image of FIG. 7.

DETAILED DESCRIPTION

The subject image enhancement system advantageously works by analysisand manipulation of numerically encoded image data, such as digitallyencoded picture data associated with the many such sources noted above.For purposes of illustration, digital images are referenced which areencoded in commonly-used RGB color space, as is typically encountered inimage capture devices or digital image processing devices. However, itis to be appreciated that the teachings herein are suitably applied toany encoded image, in any primary color scheme or in grayscale. Further,the subject system is suitably implemented on any suitable computerplatform, and will be described in conjunction with a general purposedigital computing device such as a workstation. However, as noted inmore detail below, the subject system suitably resides on a digitalimaging device, a controller of a document processing device, orimplemented directly in an image capture device, such as a digitalcamera, which device incorporates ability do perform the analysis andcalculations noted herein.

Turning now to FIG. 1, illustrated is a hardware diagram of a suitablecomputer or workstation 100 for use in connection with the subjectsystem. A suitable workstation includes a processor unit 102 which isadvantageously placed in data communication with a data storage, whichdata storage suitably includes read only memory 104, non-volatile readonly memory, volatile read only memory or a combination thereof, randomaccess memory 106, display interface 108, storage interface 110, andnetwork interface 112. In a preferred embodiment, interface to theforegoing modules is suitably accomplished via a bus 114. As will beseen below, the subject functionality is suitably implemented viainstructions read from storage, typically being run from random accessmemory 106, as will be appreciated by one of ordinary skill in the art,and the detail of which follows below.

Read only memory 104 suitably includes firmware, such as static data orfixed instructions, such as BIOS, system functions, configuration data,and other routines used for operation of the workstation 100 via CPU102.

Random access memory 106 provides a storage area for data andinstructions associated with applications and data handling accomplishedby processor 102.

Display interface 108 receives data or instructions from othercomponents on bus 114, which data is specific to generating a display tofacilitate a user interface. Display interface 108 suitably providesoutput to a display terminal 128, suitably a video display device suchas a monitor, LCD, plasma, or any other suitable visual output device aswill be appreciated by one of ordinary skill in the art.

Storage interface 110 suitably provides a mechanism for non-volatile,bulk or long term storage of data or instructions in the workstation100. Storage interface 110 suitably uses a storage mechanism, such asstorage 118, suitably comprised of a disk, tape, CD, DVD, or otherrelatively higher capacity addressable or serial storage medium.

Network interface 112 suitably communicates to at least one othernetwork interface, shown as network interface 120, such as a networkinterface card. It will be appreciated that by one or ordinary skill inthe art that a suitable network interface is comprised of both physicaland protocol layers and is suitably any wired system, such as Ethernet,token ring, or any other wide area or local area network communicationsystem, or wireless system, such as WiFi, WiMax, or any other suitablewireless network system, as will be appreciated by on of ordinary skillin the art.

An input/output interface 116 in data communication with bus 114 issuitably connected with an input device 122, such as a keyboard or thelike. Input/output interface 116 also suitably provides data output to aperipheral interface 124, such as a USB, universal serial bus output,SCSI, Firewire (IEEE 1394) output, or any other interface as may beappropriate for a selected application. Finally, input/output interface116 is suitably in data communication with a pointing device interface128 for connection with devices, such as a mouse, light pen, touchscreen, or the like.

In the illustration of FIG. 1, a network interface, such as networkinterface card 120, places the network interface 112 in datacommunication with network 132. Also in data communication with thenetwork 132 in the illustration is a digital imaging device 134, and adocument output device 136 that advantageously includes a controller138. It will be appreciated, as noted above, that devices such asdigital imaging device 134, as well as intelligent output devices, suchas printers, copiers, facsimile machines, scanners, or combinationsthereof, frequently employ intelligent controllers, such as isillustrated. It will be appreciated that any such device suitablyincludes sufficient capability to complete the image enhancementdisclosed herein. Alternatively enhancement functions are suitablydistributed between a plurality of intelligent devices placed inrelative data communication to one another.

Turning now to FIG. 2, illustrated is a flow chart of an imageenhancement operation 200 of the subject application, suitably implementfrom instructions and data associated with the workstation of FIG. 1.First, at block 202, an incoming image is received via any suitablemeans known in the art. As noted above, the incoming image is suitablyany electronic document, such as a digitally encoded image from one ormore of the plurality of sources noted above. Next, at block 204, dataof the incoming image is analyzed relative to frequency informationassociated with the encoded data. In the preferred embodiment, ahistogram is generated from this analysis, the particulars of which willbe detailed below.

Next, in FIG. 2, at block 206, a cumulative probability distributionfunction is calculated forming a histogram for spectral or image contentanalysis completed at block 204. Next, at block 208, spatial parameters,that is, characteristics as to distinctive areas associated with theimage, are calculated. A statistical determination is then made of areceived image to determine if it is backlit at 210. Upon adetermination that an image is backlit, block 212 accomplishes aconstruction or identification of a mask area of one or more backlitimage portion. The mask is suitably contiguous and blurred in a backlitimage of the preferred embodiment. While a backlit area mask is used ina preferred embodiment, it will be appreciated that a mask is suitablyeither the backlit area or frontal image area, with appropriatealgorithmic adjustments made according to which mask is chosen. Next, atblock 214, a tone modification function is applied to the backlit areain the preferred embodiment to result in an enhanced image output.

Image enhancement as noted above is suitably accomplished on metadatathat is often attached to an encoded image. However, it will beappreciated that such corrections are also suitably calculated directlyfrom image data. Devices, such as digital cameras, often include encodedimages inclusive of metadata. Images from digital capture devices, suchas digital cameras, are particularly problematic for image acquisitioninsofar backlit situations are either unavoidable, or not contemplatedby novice photographers.

The foregoing system accomplishes image enhancement by calculation ofparameters associated with an image, as well as spatially constrainedchanges that are made in tone scale rendering. The actual modificationsare made, in the preferred embodiment, by use of cumulative probabilitydistribution and spatial predictors. Additionally, it will beappreciated that if only one portion of an image suffers from tone scaleproblems, such as a sky in a backlit photograph, only this portion needbe addressed to allow for significant improvement in overall imagequality. Complementary image portions are suitably left unaltered, orsubject to image enhancement independently in a fashion appropriate foreach portion. This is to be contrasted with earlier systems whichtypically attempt to apply methods or algorithms to an entire image.Such algorithms may manipulate or adjust portions of an image that areotherwise acceptable, resulting in degradation as to those portions.

Turning now to FIG. 3, illustrated is a methodology of spectralfrequency analysis used in conjunction with the teachings of the subjectapplication. A cumulative property distribution of intensitiesassociated with image pixels advantageously provides an indicator of adegree of backlighting from a corresponding electronic image. On abacklit image, a cumulative property distribution rises more rapidly atfirst than with a well lit image. Additionally, there is often aflattening in a mid-scale range associated with the distribution. Asnoted above, a representative encoding is in connection with red-green-blue or RGB color space, which encoding is reflected in therepresentative graphs of FIG. 3, as well as those of FIGS. 4 and 8 aswill be addressed below.

FIGS. 3A and 3B illustrate histograms of two sample images for whichback lighting is present. The graphs of FIGS. 3A and 3B will beunderstood to be representative graphs only, and are given asillustrative of backlit image properties associated with the subjectapplication. In the subject examples, 8 bits are used for encoding eachof red, green and blue of the RGB encoding, each component of which isreflected in its own curve. Such 8-bit encoding allows for 256 (0-255)levels for each of the three additive primary colors. In the graphs, theabscissa values are those associated with each of the red, green andblue values. The ordinate values are a cumulative histogram associatedwith RGB values wherein the ordinate values represent a probabilitywhich is a function of a coefficient of variation which is less than anindicated corresponding RGB code value.

In the example of FIG. 3A, an associated image was that of a Hamburgcathedral which appears below in connection with FIGS. 5-7. It will benoted that the graphs here exhibit a rapid rise, flattening andsubsequent resume rise which, as noted, above, is indicative of a backlighting. The example of FIG. 3B is that of a backlit Buddha image whichalso shows an initial fast rise, followed by a subsequent flattening. Inthis example, it will be noted that no second rapidly rising area ispresent in the curves. Turning to FIG. 4, corresponding representationsof a normal, front lit image are presented with a similar graphicalformat. In these instances, it will be noted in both FIGS. 4A and 4Bthat the trend for a quick rise and subsequent flattening noted inconnection with the graphs of FIG. 3, are not found in either instance.Thus, the cumulative property distribution will be noted to provide amechanism by which front lighting and back lighting may be readilydetected.

Another consideration is an area of interest from which a cumulativeprobability distribution is taken and a relative distribution of codevalues in different areas. By way of example, if one assumesstatistically that most people take pictures with the principle subjectin the center, then a center-weighted cumulative probabilitydistribution becomes of interest. If it is a situation, such as aback-lit situation, then typically an upper portion of an image shouldhave much higher code values than a center area or a bottom area.

Turning to FIG. 5, illustrated is a representative picture of theHamburg cathedral shown, referred to graphically above, wherein backlighting is present. In the preferred embodiment, an operation is madeto identify a darker image portion as a continuous blob. A blob isdefined herein as a shape without significant holes or discontinuitiesassociated with it, typically in the center of a picture or imagesframe. As noted above, in connection with FIG. 2, in the preferredembodiment, a mask is suitably made from this blob and values are usedto change code values within the mask area in the preferred embodiment.In the event that a blob has discontinuities, a straightforwardoperation is suitably used to fill in any such discontinuity so as toarrive at continuous blob area for application of image enhancement.

FIG. 6 illustrates a suitable mask area that corresponds with the imageof FIG. 5. As noted above, code values outside an identifier mask areaare also suitably altered, such as by darkening, to improve a view ofthe background image portion. Application of lightning of the foregoingimage, darkening of the background or backlit portion of the image, or acombination thereof, is illustrated in connection with FIG. 7.Algorithms associated with lightening or darkening of images or portionsthereof are well understood by one of ordinary skill in the art. Whencompared to the image of FIG. 5, it will be appreciated that the imageof FIG. 7 is significantly improved in detail by virtue of applicationof the subject system.

Turning to FIG. 8, a representative graph of the cumulative probabilitydistribution associated with the enhanced image of FIG. 7 isillustrated. From the illustration of FIG. 8, it will be appreciatedthat the cumulative probability distribution function from the histogramof the modified picture appears more analogous to that of a normal,front lit picture as is illustrated in connection with FIGS. 4A and 4B.

The subject application extends to computer programs in the form ofsource code, object code, code intermediate sources and partiallycompiled object code, or in any other form suitable for use in theimplementation of the subject application. Computer programs aresuitably standalone applications, software components, scripts orplug-ins to other applications. Computer programs embedding the subjectapplication are advantageously embodied on a carrier, being any entityor device capable of carrying the computer program: for example, astorage medium such as ROM or RAM, optical recording media such asCD-ROM or magnetic recording media such as floppy discs. The carrier isany transmissible carrier such as an electrical or optical signalconveyed by electrical or optical cable, or by radio or other means.Computer programs are suitably downloaded across the Internet from aserver. Computer programs are also capable of being embedded in anintegrated circuit. Any and all such embodiments containing code thatwill cause a computer to perform substantially the subject applicationprinciples as described, will fall within the scope of the subjectapplication.

The foregoing description of a preferred embodiment of the subjectapplication has been presented for purposes of illustration anddescription. It is not intended to be exhaustive or to limit the subjectapplication to the precise form disclosed. Obvious modifications orvariations are possible in light of the above teachings. The embodimentwas chosen and described to provide the best illustration of theprinciples of the subject application and its practical application tothereby enable one of ordinary skill in the art to use the subjectapplication in various embodiments and with various modifications as aresuited to the particular use contemplated. All such modifications andvariations are within the scope of the subject application as determinedby the appended claims when interpreted in accordance with the breadthto which they are fairly, legally and equitably entitled.

1. A system for predictor-based image enhancement comprising: meansadapted for receiving image data, the image data including datarepresentative of a backlit image inclusive of at least one specimenarea and at least one background area; transition detection meansadapted for determining, from received image data, a transition betweenthe at least one specimen area and the at least one background area; andadjustment means adapted for adjusting a parameter of image dataassociated with at least one of the specimen area and the backgroundarea in accordance with a determined transition.
 2. The system forpredictor-based image enhancement of claim 1 wherein the adjustmentmeans includes means adapted for adjusting a lighting level associatedwith at least one of image data of the specimen area and image data ofthe background area.
 3. The system for predictor-based image enhancementof claim 1 wherein the adjustment means includes means adapted forincreasing a lighting level associated with image date of the specimenarea and decreasing a lighting level associated with image data of thebackground area.
 4. The system for predictor-based image enhancement ofclaim 1 further comprising: determining means adapted for determiningspectral frequency data representative of a spectral frequencydistribution of color data included in the image data; and wherein theadjustment means includes means adapted for adjusting the lighting levelassociated with at least one of image data of the specimen area andimage data of the background area in accordance with the spectralfrequency data.
 5. The system for predictor-based image enhancement ofclaim 4 wherein the spectral frequency data includes distribution datarepresentative of a cumulative probability distribution of intensityvalues encoded in the image data.
 6. The system for predictor-basedimage enhancement of claim 1 further comprising: mask generator meansadapted for generating mask data corresponding a determined transition;and wherein the adjustment means includes means adapted for selectivelyadjusting a parameter of image data associated with at least one of thespecimen area and the background area in accordance with a determinedtransition in accordance with the mask data.
 7. The system forpredictor-based image enhancement of claim 6 wherein the mask datacorresponds to at least one portion of an image represented by the imagedata, which at least one portion defines a shape having no significantholes or discontinuities.
 8. A method for predictor-based imageenhancement comprising the steps of: receiving image data, the imagedata including data representative of a backlit image inclusive of atleast one specimen area and at least one background area; determining,from received image data, a transition between the at least one specimenarea and the at least one background area; and adjusting a parameter ofimage data associated with at least one of the specimen area and thebackground area in accordance with a determined transition.
 9. Themethod for predictor-based image enhancement of claim 8 wherein the stepof adjusting includes adjusting a lighting level associated with atleast one of image data of the specimen area and image data of thebackground area.
 10. The method for predictor-based image enhancement ofclaim 8 wherein the step of adjusting includes increasing a lightinglevel associated with image date of the specimen area and decreasing alighting level associated with image data of the background area. 11.The method for predictor-based image enhancement of claim 8 furthercomprising the steps of: determining spectral frequency datarepresentative of a spectral frequency distribution of color dataincluded in the image data; and adjusting the lighting level associatedwith at least one of image data of the specimen area and image data ofthe background area in accordance with the spectral frequency data. 12.The method for predictor-based image enhancement of claim 11 wherein thespectral frequency data includes distribution data representative of acumulative probability distribution of intensity values encoded in theimage data.
 13. The method for predictor-based image enhancement ofclaim 8 further comprising the steps of: generating mask datacorresponding a determined transition; and selectively adjusting aparameter of image data associated with at least one of the specimenarea and the background area in accordance with a determined transitionin accordance with the mask data.
 14. The method for predictor-basedimage enhancement of claim 13 wherein the mask data corresponds to atleast one portion of an image represented by the image data, which atleast one portion defines a shape having no significant holes ordiscontinuities.
 15. A computer-implemented method for predictor-basedimage enhancement comprising the steps of: receiving image data, theimage data including data representative of a backlit image inclusive ofat least one specimen area and at least one background area;determining, from received image data, a transition between the at leastone specimen area and the at least one background area; and adjusting aparameter of image data associated with at least one of the specimenarea and the background area in accordance with a determined transition.16. The computer-implemented method for predictor-based imageenhancement of claim 15 wherein the step of adjusting includes at leastone of adjusting a lighting level associated with at least one of imagedata of the specimen area and image data of the background area andincreasing a lighting level associated with image date of the specimenarea and decreasing a lighting level associated with image data of thebackground area.
 17. The computer-implemented method for predictor-basedimage enhancement of claim 15 further comprising the steps of:determining spectral frequency data representative of a spectralfrequency distribution of color data included in the image data; andadjusting the lighting level associated with at least one of image dataof the specimen area and image data of the background area in accordancewith the spectral frequency data.
 18. The computer-implemented methodfor predictor-based image enhancement of claim 17 wherein the spectralfrequency data includes distribution data representative of a cumulativeprobability distribution of intensity values encoded in the image data.19. The computer-implemented method for predictor-based imageenhancement of claim 15 further comprising the steps of: generating maskdata corresponding a determined transition; and selectively adjusting aparameter of image data associated with at least one of the specimenarea and the background area in accordance with a determined transitionin accordance with the mask data.
 20. The computer-implemented methodfor predictor-based image enhancement of claim 19 wherein the mask datacorresponds to at least one portion of an image represented by the imagedata, which at least one portion defines a shape having no significantholes or discontinuities.